Macroalgal microbiomes unveil a valuable genetic resource for halogen metabolism.

BACKGROUND: Macroalgae, especially reds (Rhodophyta Division) and browns (Phaeophyta Division), are known for producing various halogenated compounds. Yet, the reasons underlying their production and the fate of these metabolites remain largely unknown. Some theories suggest their potential antimicrobial activity and involvement in interactions between macroalgae and prokaryotes. However, detailed investigations are currently missing on how the genetic information of prokaryotic communities associated with macroalgae may influence the fate of organohalogenated molecules. RESULTS: To address this challenge, we created a specialized dataset containing 161 enzymes, each with a complete enzyme commission number, known to be involved in halogen metabolism. This dataset served as a reference to annotate the corresponding genes encoded in both the metagenomic contigs and 98 metagenome-assembled genomes (MAGs) obtained from the microbiome of 2 red (Sphaerococcus coronopifolius and Asparagopsis taxiformis) and 1 brown (Halopteris scoparia) macroalgae. We detected many dehalogenation-related genes, particularly those with hydrolytic functions, suggesting their potential involvement in the degradation of a wide spectrum of halocarbons and haloaromatic molecules, including anthropogenic compounds. We uncovered an array of degradative gene functions within MAGs, spanning various bacterial orders such as Rhodobacterales, Rhizobiales, Caulobacterales, Geminicoccales, Sphingomonadales, Granulosicoccales, Microtrichales, and Pseudomonadales. Less abundant than degradative functions, we also uncovered genes associated with the biosynthesis of halogenated antimicrobial compounds and metabolites. CONCLUSION: The functional data provided here contribute to understanding the still largely unexplored role of unknown prokaryotes. These findings support the hypothesis that macroalgae function as holobionts, where the metabolism of halogenated compounds might play a role in symbiogenesis and act as a possible defense mechanism against environmental chemical stressors. Furthermore, bacterial groups, previously never connected with organohalogen metabolism, e.g., Caulobacterales, Geminicoccales, Granulosicoccales, and Microtrichales, functionally characterized through MAGs reconstruction, revealed a biotechnologically relevant gene content, useful in synthetic biology, and bioprospecting applications. Video Abstract.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions.

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

Inter-comparison of marine microbiome sampling protocols.

Research on marine microbial communities is growing, but studies are hard to compare because of variation in seawater sampling protocols. To help researchers in the inter-comparison of studies that use different seawater sampling methodologies, as well as to help them design future sampling campaigns, we developed the EuroMarine Open Science Exploration initiative (EMOSE). Within the EMOSE framework, we sampled thousands of liters of seawater from a single station in the NW Mediterranean Sea (Service d'Observation du Laboratoire Arago [SOLA], Banyuls-sur-Mer), during one single day. The resulting dataset includes multiple seawater processing approaches, encompassing different material-type kinds of filters (cartridge membrane and flat membrane), three different size fractionations (>0.22 µm, 0.22-3 µm, 3-20 µm and >20 µm), and a number of different seawater volumes ranging from 1 L up to 1000 L. We show that the volume of seawater that is filtered does not have a significant effect on prokaryotic and protist diversity, independently of the sequencing strategy. However, there was a clear difference in alpha and beta diversity between size fractions and between these and "whole water" (with no pre-fractionation). Overall, we recommend care when merging data from datasets that use filters of different pore size, but we consider that the type of filter and volume should not act as confounding variables for the tested sequencing strategies. To the best of our knowledge, this is the first time a publicly available dataset effectively allows for the clarification of the impact of marine microbiome methodological options across a wide range of protocols, including large-scale variations in sampled volume.

Amplicon-Based Microbiome Profiling: From Second- to Third-Generation Sequencing for Higher Taxonomic Resolution.

The 16S rRNA amplicon-based sequencing approach represents the most common and cost-effective strategy with great potential for microbiome profiling. The use of second-generation sequencing (NGS) technologies has led to protocols based on the amplification of one or a few hypervariable regions, impacting the outcome of the analysis. Nowadays, comparative studies are necessary to assess different amplicon-based approaches, including the full-locus sequencing currently feasible thanks to third-generation sequencing (TGS) technologies. This study compared three different methods to achieve the deepest microbiome taxonomic characterization: (a) the single-region approach, (b) the multiplex approach, covering several regions of the target gene/region, both based on NGS short reads, and (c) the full-length approach, which analyzes the whole length of the target gene thanks to TGS long reads. Analyses carried out on benchmark microbiome samples, with a known taxonomic composition, highlighted a different classification performance, strongly associated with the type of hypervariable regions and the coverage of the target gene. Indeed, the full-length approach showed the greatest discriminating power, up to species level, also on complex real samples. This study supports the transition from NGS to TGS for the study of the microbiome, even if experimental and bioinformatic improvements are still necessary.

Extension of the shelf-life of fresh pasta using modified atmosphere packaging and bioprotective cultures.

Microbial stability of fresh pasta depends on heat treatment, storage temperature, proper preservatives, and atmosphere packaging. This study aimed at improving the microbial quality, safety, and shelf life of fresh pasta using modified atmosphere composition and packaging with or without the addition of bioprotective cultures (Lactobacillus acidophilus, Lactobacillus casei, Bifidobacterium spp., and Bacillus coagulans) into semolina. Three fresh pasta variants were made using (i) the traditional protocol (control), MAP (20:80 CO2:N2), and barrier packaging, (ii) the experimental MAP (40:60 CO2:N2) and barrier packaging, and (iii) the experimental MAP, barrier packaging, and bioprotective cultures. Their effects on physicochemical properties (i.e., content on macro elements, water activity, headspace O2, CO2 concentrations, and mycotoxins), microbiological patterns, protein, and volatile organic compounds (VOC) were investigated at the beginning and the end of the actual or extended shelf-life through traditional and multi-omics approaches. We showed that the gas composition and properties of the packaging material tested in the experimental MAP system, with or without bioprotective cultures, positively affect features of fresh pasta avoiding changes in their main chemical properties, allowing for a storage longer than 120 days under refrigerated conditions. These results support that, although bioprotective cultures were not all able to grow in tested conditions, they can control the spoilage and the associated food-borne microbiota in fresh pasta during storage by their antimicrobials and/or fermentation products synergically. The VOC profiling, based on gas-chromatography mass-spectrometry (GC-MS), highlighted significant differences affected by the different manufacturing and packaging of samples. Therefore, the use of the proposed MAP system and the addition of bioprotective cultures can be considered an industrial helpful strategy to reduce the quality loss during refrigerated storage and to increase the shelf life of fresh pasta for additional 30 days by allowing the economic and environmental benefits spurring innovation in existing production models.

Natural and after colon washing fecal samples: the two sides of the coin for investigating the human gut microbiome.

To date several studies address the important role of gut microbiome and its interplay with the human host in the health and disease status. However, the selection of a universal sampling matrix representative of the microbial biodiversity associated with the gastrointestinal (GI) tract, is still challenging. Here we present a study in which, through a deep metabarcoding analysis of the 16S rRNA gene, we compared two sampling matrices, feces (F) and colon washing feces (CWF), in order to evaluate their relative effectiveness and accuracy in representing the complexity of the human gut microbiome. A cohort of 30 volunteers was recruited and paired F and CWF samples were collected from each subject. Alpha diversity analysis confirmed a slightly higher biodiversity of CWF compared to F matched samples. Likewise, beta diversity analysis proved that paired F and CWF microbiomes were quite similar in the same individual, but remarkable inter-individual variability occurred among the microbiomes of all participants. Taxonomic analysis in matched samples was carried out to investigate the intra and inter individual/s variability. Firmicutes, Bacteroidota, Proteobacteria and Actinobacteriota were the main phyla in both F and CWF samples. At genus level, Bacteirodetes was the most abundant in F and CWF samples, followed by Faecalibacterium, Blautia and Escherichia-Shigella. Our study highlights an inter-individual variability greater than intra-individual variability for paired F and CWF samples. Indeed, an overall higher similarity was observed across matched F and CWF samples, suggesting, as expected, a remarkable overlap between the microbiomes inferred using the matched F and CWF samples. Notably, absolute quantification of total 16S rDNA by droplet digital PCR (ddPCR) revealed comparable overall microbial load between paired F and CWF samples. We report here the first comparative study on fecal and colon washing fecal samples for investigating the human gut microbiome and show that both types of samples may be used equally for the study of the gut microbiome. The presented results suggest that the combined use of both types of sampling matrices could represent a suitable choice to obtain a more complete overview of the human gut microbiota for addressing different biological and clinical questions.

Complement downregulation promotes an inflammatory signature that renders colorectal cancer susceptible to immunotherapy.

BACKGROUND AND AIMS: The role of inflammatory immune responses in colorectal cancer (CRC) development and response to therapy is a matter of intense debate. While inflammation is a known driver of CRC, inflammatory immune infiltrates are a positive prognostic factor in CRC and predispose to response to immune checkpoint blockade (ICB) therapy. Unfortunately, over 85% of CRC cases are primarily unresponsive to ICB due to the absence of an immune infiltrate, and even the cases that show an initial immune infiltration can become refractory to ICB. The identification of therapy supportive immune responses in the field has been partially hindered by the sparsity of suitable mouse models to recapitulate the human disease. In this study, we aimed to understand how the dysregulation of the complement anaphylatoxin C3a receptor (C3aR), observed in subsets of patients with CRC, affects the immune responses, the development of CRC, and response to ICB therapy. METHODS: We use a comprehensive approach encompassing analysis of publicly available human CRC datasets, inflammation-driven and newly generated spontaneous mouse models of CRC, and multiplatform high-dimensional analysis of immune responses using microbiota sequencing, RNA sequencing, and mass cytometry. RESULTS: We found that patients' regulation of the complement C3aR is associated with epigenetic modifications. Specifically, downregulation of C3ar1 in human CRC promotes a tumor microenvironment characterized by the accumulation of innate and adaptive immune cells that support antitumor immunity. In addition, in vivo studies in our newly generated mouse model revealed that the lack of C3a in the colon activates a microbiota-mediated proinflammatory program which promotes the development of tumors with an immune signature that renders them responsive to the ICB therapy. CONCLUSIONS: Our findings reveal that C3aR may act as a previously unrecognized checkpoint to enhance antitumor immunity in CRC. C3aR can thus be exploited to overcome ICB resistance in a larger group of patients with CRC.

Farnesoid X receptor activation by the novel agonist TC-100 (3α, 7α, 11ß-Trihydroxy-6α-ethyl-5ß-cholan-24-oic Acid) preserves the intestinal barrier integrity and promotes intestinal microbial reshaping in a mouse model of obstructed bile acid flow.

The intestinal tract hosts the gut microbiota (GM), actively shaping health. Bile acids(BAs) are both digestive and signaling molecules acting as hormones via the activation of farnesoid X receptor (FXR). Obstruction of bile flow initiates a cascade of pathological events ultimately leading to intestinal mucosal injury. Administration of BAs in models of obstructed bile flow counteracts these detrimental effects. Objective of this study was to investigate the effects of the novel FXR agonist 3α, 7α, 11ß-Trihydroxy-6α-ethyl-5ß-cholan-24-oic Acid (TC-100) on intestinal mucosa integrity and cecal microbiome composition after surgical bile duct ligation (BDL), a rodent model causing bile flow obstruction. Pharmacological FXR activation was accomplished by daily oral gavage with TC-100 for 5 days. 2 days after treatment initiation, BDL was performed. BAs measurement was carried out and the 16S rDNA (V5-V6 hyper-variable regions) extracted from the cecal content was sequenced. TC-100 activates Fxr in the gut-liver axis and this translated into a significant reduction of serum and bile BA pool size with a shift to a more hydrophilic composition, while signs of intestinal mucosal damage were prevented. Firmicutes:Bacteroidota ratio progressively increased from Sham Operated (SO) mice to TC-100-treated mice. LEfSe analysis showed that Verrucomicrobia, and particularly Akkermansia muciniphila (Amuc) increasingly recognized for improving gut homeostasis and immune functions, were strongly associated to TC-100-treated mice. Intriguingly, Amuc abundance was also negatively associated to cholic acid levels. Collectively, these data indicate that intestinal FXR activation by TC-100 prevents early signs of intestinal mucosal damage by modulating BA homeostasis and GM composition.

Machine Learning Data Analysis Highlights the Role of Parasutterella and Alloprevotella in Autism Spectrum Disorders.

In recent years, the involvement of the gut microbiota in disease and health has been investigated by sequencing the 16S gene from fecal samples. Dysbiotic gut microbiota was also observed in Autism Spectrum Disorder (ASD), a neurodevelopmental disorder characterized by gastrointestinal symptoms. However, despite the relevant number of studies, it is still difficult to identify a typical dysbiotic profile in ASD patients. The discrepancies among these studies are due to technical factors (i.e., experimental procedures) and external parameters (i.e., dietary habits). In this paper, we collected 959 samples from eight available projects (540 ASD and 419 Healthy Controls, HC) and reduced the observed bias among studies. Then, we applied a Machine Learning (ML) approach to create a predictor able to discriminate between ASD and HC. We tested and optimized three algorithms: Random Forest, Support Vector Machine and Gradient Boosting Machine. All three algorithms confirmed the importance of five different genera, including Parasutterella and Alloprevotella. Furthermore, our results show that ML algorithms could identify common taxonomic features by comparing datasets obtained from countries characterized by latent confounding variables.

Analysis of microbiome in gastrointestinal stromal tumors: Looking for different players in tumorigenesis and novel therapeutic options.

Preclinical forms of gastrointestinal stromal tumor (GIST), small asymptomatic lesions, called microGIST, are detected in approximately 30% of the general population. Gastrointestinal stromal tumor driver mutation can be already detected in microGISTs, even if they do not progress into malignant cancer; these mutations are necessary, but insufficient events to foster tumor progression. Here we profiled the tissue microbiota of 60 gastrointestinal specimens in three different patient cohorts-micro, low-risk, and high-risk or metastatic GIST-exploring the compositional structure, predicted function, and microbial networks, with the aim of providing a complete overview of microbial ecology in GIST and its preclinical form. Comparing microGISTs and GISTs, both weighted and unweighted UniFrac and Bray-Curtis dissimilarities showed significant community-level separation between them and a pronounced difference in Proteobacteria, Firmicutes, and Bacteroidota was observed. Through the LEfSe tool, potential microbial biomarkers associated with a specific type of lesion were identified. In particular, GIST samples were significantly enriched in the phylum Proteobacteria compared to microGISTs. Several pathways involved in sugar metabolism were also highlighted in GISTs; this was expected as cancer usually displays high aerobic glycolysis in place of oxidative phosphorylation and rise of glucose flux to promote anabolic request. Our results highlight that specific differences do exist in the tissue microbiome community between GIST and benign lesions and that microbiome restructuration can drive the carcinogenesis process.

Comparative Fecal Microbiota Analysis of Infants With Acute Bronchiolitis Caused or Not Caused by Respiratory Syncytial Virus.

Bronchiolitis due to respiratory syncytial virus (RSV) or non-RSV agents is a health-menacing lower respiratory tract (LRT) disease of infants. Whereas RSV causes more severe disease than other viral agents may, genus-dominant fecal microbiota profiles have been identified in US hospitalized infants with bronchiolitis. We investigated the fecal microbiota composition of infants admitted to an Italian hospital with acute RSV (25/37 [67.6%]; group I) or non-RSV (12/37 [32.4%]; group II) bronchiolitis, and the relationship of fecal microbiota characteristics with the clinical characteristics of infants. Group I and group II infants differed significantly (24/25 [96.0%] versus 5/12 [41.7%]; P = 0.001) regarding 90% oxygen saturation (SpO2), which is an increased respiratory effort hallmark. Accordingly, impaired feeding in infants from group I was significantly more frequent than in infants from group II (19/25 [76.0%] versus 4/12 [33.3%]; P = 0.04). Conversely, the median (IQR) length of stay was not significantly different between the two groups (seven [3-14] for group I versus five [5-10] for group II; P = 0.11). The 16S ribosomal RNA V3-V4 region amplification of infants' fecal samples resulted in 299 annotated amplicon sequence variants. Based on alpha- and beta-diversity microbiota downstream analyses, group I and group II infants had similar bacterial communities in their samples. Additionally, comparing infants having <90% SpO2 (n = 29) with infants having ≥90% SpO2 (n = 8) showed that well-known dominant genera (Bacteroides, Bifidobacterium, Escherichia/Shigella, and Enterobacter/Veillonella) were differently, but not significantly (P = 0.44, P = 0.71, P = 0.98, and P = 0.41, respectively) abundant between the two subgroups. Overall, we showed that, regardless of RSV or non-RSV bronchiolitis etiology, no fecal microbiota-composing bacteria could be associated with the severity of acute bronchiolitis in infants. Larger and longitudinally conducted studies will be necessary to confirm these findings.

Morphological, molecular, and biochemical study of cyanobacteria from a eutrophic Algerian reservoir (Cheffia).

The cyanobacteria management in water bodies requires a deep knowledge of the community composition. Considering the reliable and thorough information provided by the polyphasic approach in cyanobacteria taxonomy, here we assess the cyanobacterial community structure of the Cheffia reservoir from Algeria. Cyanobacteria were identified on the basis of morphological traits and next-generation sequencing (NGS); toxins-related genes were localized in addition to the identification of toxins; temperature and nutrient level of water samples were also determined. The polyphasic approach was essential for cyanobacteria investigation; 28 genera were identified through 16S rRNA metabarcoding with the dominance of taxa from Microcystis (34.2%), Aphanizomenon (20.1%), and Planktothrix (20.0%), and morphological analysis revealed the association in this water body of five species within the genus Microcystis: M. aeruginosa, M. novacekii, M. panniformis, M. ichthyoblabe, and M. flos-aquae. The presence of mcyE genotypes was detected; moreover, HPLC-PDA and LC-ESI-MS/MS revealed the production of microcystin-LR. Results obtained in our study are very important since this ecosystem is used for water supply and irrigation; as a consequence, a good water management plan is essential.

Microbiome composition indicate dysbiosis and lower richness in tumor breast tissues compared to healthy adjacent paired tissue, within the same women.

BACKGROUND: Breast cancer (BC) is the most common malignancy in women, in whom it reaches 20% of the total neoplasia incidence. Most BCs are considered sporadic and a number of factors, including familiarity, age, hormonal cycles and diet, have been reported to be BC risk factors. Also the gut microbiota plays a role in breast cancer development. In fact, its imbalance has been associated to various human diseases including cancer although a consequential cause-effect phenomenon has never been proven. METHODS: The aim of this work was to characterize the breast tissue microbiome in 34 women affected by BC using an NGS-based method, and analyzing the tumoral and the adjacent non-tumoral tissue of each patient. RESULTS: The healthy and tumor tissues differed in bacterial composition and richness: the number of Amplicon Sequence Variants (ASVs) was higher in healthy tissues than in tumor tissues (p = 0.001). Moreover, our analyses, able to investigate from phylum down to species taxa for each sample, revealed major differences in the two richest phyla, namely, Proteobacteria and Actinobacteria. Notably, the levels of Actinobacteria and Proteobacteria were, respectively, higher and lower in healthy with respect to tumor tissues. CONCLUSIONS: Our study provides information about the breast tissue microbial composition, as compared with very closely adjacent healthy tissue (paired samples within the same woman); the differences found are such to have possible diagnostic and therapeutic implications; further studies are necessary to clarify if the differences found in the breast tissue microbiome are simply an association or a concausative pathogenetic effect in BC. A comparison of different results on similar studies seems not to assess a universal microbiome signature, but single ones depending on the environmental cohorts' locations.

Evaluating the Efficiency of DNA Metabarcoding to Analyze the Diet of Hippocampus guttulatus (Teleostea: Syngnathidae).

Seahorses are considered a flagship species for conservation efforts and due to their conservation status, improving knowledge on their dietary composition while applying a non-invasive approach, could be useful. Using Hippocampus guttulatus as a case study, the present study represents pioneering research into investigating the diet of seahorses by NGS-based DNA metabarcoding of fecal samples. The study developed and tested the protocol for fecal DNA metabarcoding during the feeding trials where captive seahorses were fed on a diet of known composition; the process was subsequently applied on fecal samples collected from wild individuals. The analysis of samples collected during the feeding trials indicated the reliability of the applied molecular approach by allowing the characterization of the effectively ingested prey. In the field study, among detected prey species, results revealed that the majority of the seahorse samples contained taxa such as Amphipoda, Decapoda, Isopoda, and Calanoida, while less common prey taxa were Gastropoda and Polyplacophora. As only a small amount of starting fecal material is needed and the sampling procedure is neither invasive nor lethal. The present study indicates DNA metabarcoding as useful for investigating seahorse diet and could help define management and conservation actions.

Accurate detection and quantification of SARS-CoV-2 genomic and subgenomic mRNAs by ddPCR and meta-transcriptomics analysis.

SARS-CoV-2 replication requires the synthesis of a set of structural proteins expressed through discontinuous transcription of ten subgenomic mRNAs (sgmRNAs). Here, we have fine-tuned droplet digital PCR (ddPCR) assays to accurately detect and quantify SARS-CoV-2 genomic ORF1ab and sgmRNAs for the nucleocapsid (N) and spike (S) proteins. We analyzed 166 RNA samples from anonymized SARS-CoV-2 positive subjects and we observed a recurrent and characteristic pattern of sgmRNAs expression in relation to the total viral RNA content. Additionally, expression profiles of sgmRNAs, as determined by meta-transcriptomics sequencing of a subset of 110 RNA samples, were highly correlated with those obtained by ddPCR. By providing a comprehensive and dynamic snapshot of the levels of SARS-CoV-2 sgmRNAs in infected individuals, our results may contribute a better understanding of the dynamics of transcription and expression of the genome of SARS-CoV-2 and facilitate the development of more accurate molecular diagnostic tools for the stratification of COVID-19 patients.

A primer on machine learning techniques for genomic applications.

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous "omic" data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available.

ITSoneWB: profiling global taxonomic diversity of eukaryotic communities on Galaxy.

SUMMARY: ITSoneWB (ITSone WorkBench) is a Galaxy-based bioinformatic environment where comprehensive and high-quality reference data are connected with established pipelines and new tools in an automated and easy-to-use service targeted at global taxonomic analysis of eukaryotic communities based on Internal Transcribed Spacer 1 variants high-throughput sequencing. AVAILABILITY AND IMPLEMENTATION: ITSoneWB has been deployed on the INFN-Bari ReCaS cloud facility and is freely available on the web at http://itsonewb.cloud.ba.infn.it/galaxy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Stem Cell Impairment at the Host-Microbiota Interface in Colorectal Cancer.

Colorectal cancer (CRC) initiation is believed to result from the conversion of normal intestinal stem cells (ISCs) into cancer stem cells (CSCs), also known as tumor-initiating cells (TICs). Hence, CRC evolves through the multiple acquisition of well-established genetic and epigenetic alterations with an adenoma-carcinoma sequence progression. Unlike other stem cells elsewhere in the body, ISCs cohabit with the intestinal microbiota, which consists of a diverse community of microorganisms, including bacteria, fungi, and viruses. The gut microbiota communicates closely with ISCs and mounting evidence suggests that there is significant crosstalk between host and microbiota at the ISC niche level. Metagenomic analyses have demonstrated that the host-microbiota mutually beneficial symbiosis existing under physiologic conditions is lost during a state of pathological microbial imbalance due to the alteration of microbiota composition (dysbiosis) and/or the genetic susceptibility of the host. The complex interaction between CRC and microbiota is at the forefront of the current CRC research, and there is growing attention on a possible role of the gut microbiome in the pathogenesis of CRC through ISC niche impairment. Here we primarily review the most recent findings on the molecular mechanism underlying the complex interplay between gut microbiota and ISCs, revealing a possible key role of microbiota in the aberrant reprogramming of CSCs in the initiation of CRC. We also discuss recent advances in OMICS approaches and single-cell analyses to explore the relationship between gut microbiota and ISC/CSC niche biology leading to a desirable implementation of the current precision medicine approaches.

Microbiome as Mediator of Diet on Colorectal Cancer Risk: The Role of Vitamin D, Markers of Inflammation and Adipokines.

Obesity and diet are associated with colorectal cancer (CRC) risk, and microbiome could mediate this risk factor. To investigate this interaction, we performed a case-control study (34 CRC cases and 32 controls) and analyzed fecal microbiota composition using 16S rRNA metabarcoding and sub-sequential shotgun analyses of genomic bacterial DNA to evaluate the role of microbiome and diet in CRC etiology, taking into account vitamin D and other risk biomarkers. Dietary habits were evaluated using a short questionnaire. Multivariate methods for data integration and mediation analysis models were used to investigate causal relationships. CRC cases were significantly more often deficient in vitamin D than controls (p = 0.04); FokI and CYP24A1 polymorphism frequency were different between cases and controls (p = 0.03 and p = 0.02, respectively). A diet poor in fatty fish and rich in carbohydrates was found to be significantly associated with CRC risk (p = 0.011). The mediation analysis confirmed the significant role of the microbiome in mediating CRC risk-increasing levels of Bifidobacteria/Escherichia genera ratio, an indicator of "healthy" intestinal microbiome, can overcome the effect of diet on CRC risk (p = 0.03). This study suggests that microbiome mediates the diet effect on CRC risk, and that vitamin D, markers of inflammation, and adipokines are other factors to consider in order to achieve a better knowledge of the whole carcinogenic process.

Accurate quantification of bacterial abundance in metagenomic DNAs accounting for variable DNA integrity levels.

The quantification of the total microbial content in metagenomic samples is critical for investigating the interplay between the microbiome and its host, as well as for assessing the accuracy and precision of the relative microbial composition which can be strongly biased in low microbial biomass samples. In the present study, we demonstrate that digital droplet PCR (ddPCR) can provide accurate quantification of the total copy number of the 16S rRNA gene, the gene usually exploited for assessing total bacterial abundance in metagenomic DNA samples. Notably, using DNA templates with different integrity levels, as measured by the DNA integrity number (DIN), we demonstrated that 16S rRNA copy number quantification is strongly affected by DNA quality and determined a precise correlation between quantification underestimation and DNA degradation levels. Therefore, we propose an input DNA mass correction, according to the observed DIN value, which could prevent inaccurate quantification of 16S copy number in degraded metagenomic DNAs. Our results highlight that a preliminary evaluation of the metagenomic DNA integrity should be considered before performing metagenomic analyses of different samples, both for the assessment of the reliability of observed differential abundances in different conditions and to obtain significant functional insights.